Human Approval Queues for AI Workflows
Approval queues are where AI work becomes operationally safe. Rocky can draft, classify, summarize, and prepare next actions while a human makes the final call.
When this matters
This page is for operators who want Rocky to produce usable work without turning the system into a mystery box. Use it when you need a practical path, a clear verification step, and a boundary between suggestion and action.
The operating pattern
- Classify risk. Group actions into safe read-only, reversible, customer-facing, financial, and account-level.
- Create queue states. Use states like drafted, needs review, approved, sent, rejected, and escalated.
- Show evidence. Put the source message, key facts, and recommended action next to the approval button.
- Log decisions. Record who approved, when, and why.
- Start conservative. Add automation only after the queue has produced clean repeatable decisions.
Pre-flight checklist
- Every approval has source evidence
- Buttons say exactly what they will do
- Rejected actions are retained for audit
- High-risk actions require a second look
- Notifications are quiet unless urgent
Common failure modes
- Rubber-stamp UI: If every button says approve without context, the queue is unsafe.
- Hidden side effects: Users should never wonder whether clicking a button sends, buys, deletes, or publishes.
- No audit trail: A useful queue records decisions so the system improves later.
Verification
A page is not done because it was drafted. Verify the source, run the workflow, inspect the output, and record what changed. If a step touches money, customers, accounts, permissions, or private data, keep it behind an explicit human approval gate.